DataStax Enterprise 5.0 Analytics includes integration with Apache Spark. Starting with this version Hadoop is deprecated for use with DataStax Enterprise. DSE Hadoop and BYOH (Bring Your Own Hadoop) are also
deprecated.

DSE Search simplifies using search applications for data that is stored in a Cassandra database. DSE Search is an
enterprise grade search solution that is scalable to work across multiple datacenters and the cloud. DSE Search integrates
Solr to manage search indexes with a persistent store.

Options for storing field cache

Monitor the status of the field cache and set options for storing the cache on disk or
on the heap.

The Solr field cache caches values for all indexed documents, which if left unchecked, can
result in out-of-memory errors. For example, when performing faceted queries using multi-valued
fields the multiValued fields are multi-segmented (as opposed to single segmented single-valued
fields), resulting in an inefficient near real time (NRT) performance. You can use densely packed
DocValue field types and per-segment docsets. Facet queries will be per-segment, which improves
real-time search performance problems.

To ensure that the JVM heap can accommodate the cache, monitor the status of the field cache
and take advantage of the Solr option for storing the cache on disk or on the heap. To view
the status of the field cache memory usage, append &memory=true to the
URL used to view the status of Solr cores. For example, to view the field cache memory usage
of the DSE Search quick start example after running a few facet queries, use this
URL:

http://localhost:8983/solr/admin/cores?action=STATUS&memory=true

Example 1

For example, the URL for viewing the field cache memory usage in JSON format and the output
is:

Using the field cache

In Lucene-Solr 4.5 and later, docValues are mostly disk-based to avoid the requirement for
large heap allocations in Solr. If you use the field cache in sort, stats, and other queries,
make those fields docValues.